Image analysis of multiband images of geographic regions

ABSTRACT

Computer-implemented methods and systems for image analysis of multiband images of geographic regions are described, including a method by one or more computer executing executable instructions stored in one or more non-transitory, tangible, computer readable media, the method comprising: receiving one or more multiband image of a geographic region, the one or more multiband image having pixels; generating a grey level co-occurrence matrix for the pixels in the one or more multiband image; generating a surface index for the one or more multiband image containing information indicative of a surface type represented by one or more of the pixels in the one or more multiband image; and classifying the pixels of the one or more multiband image into one of a group of predefined land cover classes, based on the surface index in combination with the grey level co-occurrence matrix.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to, and is a continuation ofU.S. patent application Ser. No. 16/377,843, filed Apr. 8, 2019, whichis a continuation of U.S. patent application Ser. No. 15/440,084, filedFeb. 23, 2017, now U.S. Pat. No. 10,255,296, entitled “System and Methodfor Managing GeoDemographic Data”, which claims priority to U.S.Provisional Application No. 62/299,717, filed Feb. 25, 2016, the entiredisclosures of each of which are hereby incorporated herein byreference.

BACKGROUND

The present invention generally deals with systems and method ofmanaging a combination of geographic data and demographic data, orgeodemographic data.

The world is becoming more data driven. The data in various public andprivate databases is only as valuable as the information that may begleaned from them. Many companies provide data products by miningspecific databases for predetermined purposes. Such data mining is usedto create data packages for sale or use.

Further, others have created markets by fusing data provided by multipledata providers. However, such products derived by such fused datasources are costly, as prices multiply with the number of data sources.

There exists a need to provide an improved method and apparatus ofmanaging fused data.

SUMMARY

The present invention provides an improved method and apparatus forcreating a fused geodemographic data product.

Various embodiments described herein are drawn to a device that includesan image data receiving component operable to receive multiband imagedata of a geographic region; a surface index generation componentoperable to generate a surface index based on at least a portion of thereceived multiband image data; a classification component operable togenerate a surface cover classification based on the surface index; asegment data receiving component operable to receive segment datarelating to at least a portion of the geographic region; a zonalstatistics component operable to generate a segment surface coverclassification based on the surface cover classification and the segmentdata; a feature data receiving component operable to receive featuredata; a feature index generation component operable to generate afeature index based on the received feature data; and a catalogcomponent operable to generate a segment feature index based on thefeature index and the segment surface cover classification.

BRIEF SUMMARY OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The accompanying drawings, which are incorporated in and form a part ofthe specification, illustrate an exemplary embodiment of the presentinvention and, together with the description, serve to explain theprinciples of the invention. In the drawings:

FIG. 1 illustrates an example system for managing geodemographic data inaccordance with aspects of the present invention;

FIG. 2 illustrates an example method of managing geodemographic data inaccordance with aspects of the present invention;

FIG. 3 illustrates an example of the database of FIG. 1;

FIG. 4 illustrates an example of the accessing component of FIG. 1;

FIG. 5 illustrates a satellite image of a plot of land;

FIG. 6 illustrates another example system for managing geodemographicdata in accordance with aspects of the present invention;

FIG. 7 illustrates the classification component and voting component ofthe system of FIG. 6;

FIG. 8 illustrates a classified image of the plot of land within thesatellite image of FIG. 5; and

FIG. 9 illustrates the catalog component of the system of FIG. 1.

DETAILED DESCRIPTION

Aspects of the present invention are drawn to a system and method formanaging geodemographic data.

A first aspect of the present invention is drawn to using a grey levelco-occurrence matrix (GLCM) to additionally help identify pixels in animage. A classification component in accordance with aspects of thepresent invention is able to classify each pixel of an image in view ofthe vegetation index in combination with results from the GLCM. Theadditional information provided by the GLCM reduces the likelihood thata pixel will be incorrectly classified.

Another aspect of the present invention is drawn to using a plurality ofclassification components to classify each pixel and then determiningthe final surface cover classification based on a majority vote of theplurality of classifications for each pixel. Is should be noted that, insome embodiments, this is on a class by class basis, not on the wholeimage. In some embodiments, the image is broken up into different classimages and is then reassembled.

As mentioned above, there are many classification methods, each withrespective strengths and weaknesses. In accordance with aspects of thepresent invention, a pixel of an image may be classified by at leastthree classification components. If one of the three resultingclassifications is different from the other two, it is ignored. In otherwords the majority of the two similar classifications of the pixel willincrease the likelihood that the pixel will be classified correctly.

Another aspect of the present invention is drawn to determining featureswithin an area of land within a satellite image and providing an indexof the features per segment of land, or a segment feature index, of thearea of land within satellite image. The segment feature index mayinclude a primary feature index, a secondary feature index and atertiary feature index.

The primary feature index relates to raw tallies of features fromfeature data per land segment, which will illustrate measured featuresper land segment. For example, a raw tally of features may be that, ofthe 127 parcels of land within the area of the satellite image, thereare 120 houses and 20 in-ground pools.

The secondary feature index relates predetermined Boolean relationshipsof features from the feature data per land segment, which willillustrate predetermined associations of measured features per landsegment. For example, a predetermined Boolean relationship of featuresfrom the feature data per land segment may be that of the 127 parcels ofland within the area of the satellite image, 7 have houses AND anin-ground pool.

The tertiary feature index relates to predetermined likelihoods ofBoolean relationships of features from the feature data per landsegment, which will infer associations of measured features per landsegment. For example, many Boolean relationships may be determined, butonly a single one provides a likelihood that surpasses a predeterminedlikelihood threshold. For example, for purposes of discussion, supposethe predetermined likelihood threshold is 60%, meaning that any Booleanrelationship having a likelihood below 60% would be ignored. Further,for this discussion, presume that a single Boolean relationship offeatures from the feature data per land segment is greater than 60%, andindicates that that there is a 65% likelihood that one of the 7 havehouses having an in-ground pool will purchase a car costing at least$45,000.

Aspects of the present invention will now be described with reference toFIGS. 1-9.

FIG. 1 illustrates a system 100 for managing geodemographic data inaccordance with aspects of the present invention.

As shown in the figure, system 100 includes a data managing component102 and a network 104. Data managing component 102 includes a database106, a controlling component 108, an accessing component 110, acommunication component 112, a surface index generation (SIG) component114, a gray level co-occurrence (GLC) matrix generation component 116, aclassification component 118, a zonal statistics component 120, afeature index generation (FIG) component 122 and a catalog component124.

In this example, database 106, controlling component 108, accessingcomponent 110, communication component 112, SIG component 114, GLCmatrix generation component 116, classification component 118, zonalstatistics component 120, FIG component 122 and catalog component 124are illustrated as individual devices. However, in some embodiments, atleast two of database 106, controlling component 108, accessingcomponent 110, communication component 112, SIG component 114, GLCmatrix generation component 116, classification component 118, zonalstatistics component 120, FIG component 122 and catalog component 124may be combined as a unitary device. Further, in some embodiments, atleast one of database 106, controlling component 108, accessingcomponent 110, communication component 112, SIG component 114, GLCmatrix generation component 116, classification component 118, zonalstatistics component 120, FIG component 122 and catalog component 124may be implemented as a computer having tangible computer-readable mediafor carrying or having computer-executable instructions or datastructures stored thereon. Such tangible computer-readable media can beany available media that can be accessed by a general purpose or specialpurpose computer. Non-limiting examples of tangible computer-readablemedia include physical storage and/or memory media such as RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium which can be used tocarry or store desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Forinformation transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a computer, the computer may properly viewthe connection as a computer-readable medium. Thus, any such connectionmay be properly termed a computer-readable medium. Combinations of theabove should also be included within the scope of computer-readablemedia.

Controlling component 108 is in communication with each of accessingcomponent 110, communication component 112, SIG component 114, GLCmatrix generation component 116, classification component 118, zonalstatistics component 120, FIG component 122 and catalog component 124 bycommunication channels (not shown). Controlling component 108 may be anydevice or system that is able to control operation of each of accessingcomponent 110, communication component 112, SIG component 114, GLCmatrix generation component 116, classification component 118, zonalstatistics component 120, FIG component 122 and catalog component 124.

Accessing component 110 is arranged to bi-directionally communicate withdatabase 106 via a communication channel 126 and is arranged tobi-directionally communicate with communication component 112 via acommunication channel 128. Accessing component 110 is additionallyarranged to communicate with SIG component 114 and GLC matrix generationcomponent 116 via a communication channel 130, to communicate withclassification component 118 via a communication channel 132, tocommunicate with zonal statistics component 120 via a communicationchannel 134 and to communicate with FIG component 122 via acommunication channel 136. Accessing component 110 may be any device orsystem that is able to access data within database 106 directly viacommunication channel 126 or indirectly, via communication channel 128,communication component 112, a communication channel 138, network 104and communication channel 140.

Communication component 112 is additionally arranged to bi-directionallycommunicate with network 104 via communication channel 138.Communication component 112 may be any device or system that is able tobi-directionally communicate with network 104 via communication channel138.

Network 104 is additionally arranged to bi-directionally communicatewith database 106 via communication channel 140. Network 104 may be anyof known various communication networks, non-limiting examples of whichinclude a Local Area Network (LAN), a Wide Area Network (WAN), awireless network and combinations thereof. Such networks may supporttelephony services for a mobile terminal to communicate over a telephonynetwork (e.g., Public Switched Telephone Network (PSTN). Non-limitingexample wireless networks include a radio network that supports a numberof wireless terminals, which may be fixed or mobile, using various radioaccess technologies. According to some example embodiments, radiotechnologies that can be contemplated include: first generation (1G)technologies (e.g., advanced mobile phone system (AMPS), cellulardigital packet data (CDPD), etc.), second generation (2G) technologies(e.g., global system for mobile communications (GSM), interim standard95 (IS-95), etc.), third generation (3G) technologies (e.g., codedivision multiple access 2000 (CDMA2000), general packet radio service(GPRS), universal mobile telecommunications system (UMTS), etc.), 4G,etc. For instance, various mobile communication standards have beenintroduced, such as first generation (1G) technologies (e.g., advancedmobile phone system (AMPS), cellular digital packet data (CDPD), etc.),second generation (2G) technologies (e.g., global system for mobilecommunications (GSM), interim standard 95 (IS-95), etc.), thirdgeneration (3G) technologies (e.g., code division multiple access 2000(CDMA2000), general packet radio service (GPRS), universal mobiletelecommunications system (UMTS), etc.), and beyond 3G technologies(e.g., third generation partnership project (3GPP) long term evolution(3GPP LTE), 3GPP2 universal mobile broadband (3GPP2 UMB), etc.).

Complementing the evolution in mobile communication standards adoption,other radio access technologies have also been developed by variousprofessional bodies, such as the Institute of Electrical and ElectronicEngineers (IEEE), for the support of various applications, services, anddeployment scenarios. For example, the IEEE 802.11 standard, also knownas wireless fidelity (WiFi), has been introduced for wireless local areanetworking, while the IEEE 802.16 standard, also known as worldwideinteroperability for microwave access (WiMAX) has been introduced forthe provision of wireless communications on point-to-point links, aswell as for full mobile access over longer distances. Other examplesinclude Bluetooth™, ultra-wideband (UWB), the IEEE 802.22 standard, etc.

SIG component 114 is additionally arranged to communicate withclassification component 118 via a communication channel 142. SIGcomponent 114 may be any device or system that is able to generate asurface index, or a normalized difference surface index, a non-limitingexample of which includes a normalized difference vegetation index(NDVI). An NDVI is a simple graphical indicator that can be used toanalyze remote sensing measurements, typically not necessarily form aspace platform, and assess whether the target being observed containslive green vegetation or not. In an example embodiment, a normalizeddifference vegetation index is generated using the following equation:

(v _(NIR) −v _(R))/(v _(NIR) +v _(R)),  (1)

where v_(NIR) is the near infrared band and where v_(R) is the red band.

GLC matrix generation component 116 is additionally arranged tocommunicate with classification component 118 via a communicationchannel 144. GLC matrix generation component 116 may be any device orsystem that is able to generate a GLC matrix image band. GLC matrixgeneration component 116 provides a series of “second order” texturecalculations, and considers the relationship between groups of twopixels in the original image to generate a GLC matrix image band. GLCmatrix generation component 116 considers the relation between twopixels at a time, called the reference and the neighbor pixel. Eachpixel is the reference pixel at some point in the calculation. Theresult of this process is a plurality of measures for each pixel thatindicates a type of relationship between that pixel and its neighbors,and most measures are weighted averages of the normalized GLC matrixcell contents.

In an example embodiment, GLC matrix generation component 116 provides17 measures for each pixel. These measures are split into threecategories: contrast, orderliness and statistics. The contrast groupincludes a contrast band, a dissimilarity band, a homogeneity band andan inertia band. The orderliness group includes an angular second moment(ASM) with energy band—also called “uniformity band,” a maximumprobability (MAX) band, an entropy (ENT) band, a sum of entropy (SENT)band and a difference of entropy (DENT) band. The statistics groupincludes an average (MEAN) band, a variance (VAR) band—also known as the“sum of squares variance” band, a correlation (CORR) band, a maximumcorrelation coefficient (MaxCORR) band, an information measures ofcorrelation 1 (imcorr1) band, an information measures of correlation 2(imcorr2) band, a sum of average (SAVG) band, an sum of variance (SVAR)band and a difference of variance (DVAR) band. In an example embodiment,out of the 18 bands, three are used, one for each category.

Classification component 118 is additionally arranged to communicatewith zonal statistics component 120 via a communication channel 146.Classification component 118 may be any device or system that is able toclassify each pixel, or group of pixels, of an image as one of the groupof predefined land cover classes. In some non-limiting examples,classification component 118 is able to classify each pixel as one ofthe group consisting of consisting of grass, a tree, a shrub, a pavedsurface, a man-made pool, a natural water body and artificial turf.

Zonal statistics component 120 is additionally arranged to communicatewith FIG component 122 via a communication channel 148. Zonal statisticscomponent 120 may be any device or system that is able to generate aland cover classification per segment of land. For example, zonalstatistics component 120 may determine that a specific county, as thesegment of land, has 38% tree cover, 18% shrub cover, 16% blacktopcover, 12% grass cover, 8% natural water cover and 8% man-made structurecover based on the classification of the pixels of the image within thecounty as defined by the segment data. In some embodiments, zonalstatistics component 11 may determine the percentages of cover bydividing the number of pixels of the image within the segment by thenumber of pixels of a particular type of classification (cover).

FIG component 122 is additionally arranged to communicate with catalogcomponent 124 via a communication channel 150. FIG component 122 may beany device or system that is able to generate a feature index of thearea of land. For example, FIG component 122 may generate a featureindex indicating the number of houses, the number of plots of land withover 1 acre AND an in-ground swimming pool, and a likely number ofhouses that will purchase a riding lawn mower based on plot size andannual income.

Catalog component 124 is additionally arranged to communicate withcommunication component 112 via a communication channel 152. Catalogcomponent 124 may be any device or system that is able to generate afeature index per segment of land.

Communication channels 126, 128, 130, 132, 134, 136, 138, 140, 142, 144,146, 148, 150 and 152 may be any known wired or wireless communicationchannel.

Operation of system 100 will now be described with reference to FIGS.2-9.

FIG. 2 illustrates a method 200 of managing geodemographic data.

As shown in the figure, method 200 starts (S202) and image data isreceived (S204). For example, as shown in FIG. 1, accessing component110 retrieves image data from database 106. In some embodiments,accessing component 110 may retrieve the image data directly fromdatabase 106 via communication channel 126. In other embodiments,accessing component 110 may retrieve the image data from database 106via a path of communication channel 128, communication component 112,communication channel 138, network 104 and communication channel 140.

Database 106 may have various types of data stored therein. This will befurther described with reference to FIG. 3.

FIG. 3 illustrates an example of database 106 of FIG. 1.

As shown in FIG. 3, database 106 includes an image data database 302, atraining data database 304, a segment data database 306 and a featuredata database 308. In this example, image data database 302, trainingdata database 304, segment data database 306 and feature data database308 are illustrated as individual devices. However, in some embodiments,at least two of image data database 302, training data database 304,segment data database 306 and feature data database 308 may be combinedas a unitary device. Further, in some embodiments, at least one of imagedata database 302, training data database 304, segment data database 306and feature data database 308 may be implemented as a computer havingtangible computer-readable media for carrying or havingcomputer-executable instructions or data structures stored thereon.

Image data database 302 includes image data corresponding to an area ofland for which water is to be managed. The image data may be providedvia a satellite imaging platform. The image data may include a singleband or multi-band image data, wherein the image (of the same area ofland for which water is to be managed) is imaged in a more than onefrequency. In some embodiments, image data may include 4-band imagedata, which include red, green, blue and near infrared bands (RGB-NIR)of the same area of land for which water is to be managed. In otherembodiments, the image data may include more than 4 bands, e.g.,hyperspectral image data. The image data comprises pixels, each of whichincludes respective data values for frequency (color) and intensity(brightness). The frequency may include a plurality of frequencies,based on the number of bands used in the image data. Further, there maybe a respective intensity value for each frequency value.

Training data database 304 includes training data to train aclassification component to distinctly classify an image pixel. Forexample, training data for a 4-band image may include specific 4-bandpixels data values associated with each land cover classification. Inother words, there may be training data for a pixel associated with animage of a tree and different training data for a pixel associated witha man-made surface such as blacktop.

Segment data database 306 includes geographically divided portions ofthe land. This may be provided by government agencies or publicutilities. Non-limiting examples of geographically divided portionsinclude country, state, county, township, city or individual land ownerborders.

Feature data database 308 includes feature data associated with thegeographic area that may be provided by any known source, non-limitingexamples of which include weather data, demographic data, social data,census data, tax data and open source data.

Returning to FIG. 1, in some cases, database 106 is included in datamanaging component 102. However, in other cases, database 106 isseparated from data managing component 102, as indicated by dottedrectangle 154.

As accessing component 110 will be accessing many types of data fromdatabase 106, accessing component 110 includes many data managingcomponents. This will be described with greater detail with reference toFIG. 4.

FIG. 4 illustrates an example of accessing component 110 of FIG. 1.

As shown in FIG. 4, accessing component 110 includes a communicationcomponent 402, an image data receiving component 404, a training datareceiving component 406, a segment data receiving component 408 and afeature data receiving component 410.

In this example, communication component 402, image data receivingcomponent 404, training data receiving component 406, segment datareceiving component 408 and feature data receiving component 410 areillustrated as individual devices. However, in some embodiments, atleast two of communication component 402, image data receiving component404, training data receiving component 406, segment data receivingcomponent 408 and feature data receiving component 410 may be combinedas a unitary device. Further, in some embodiments, at least one ofcommunication component 402, image data receiving component 404,training data receiving component 406, segment data receiving component408 and feature data receiving component 410 may be implemented as acomputer having tangible computer-readable media for carrying or havingcomputer-executable instructions or data structures stored thereon.

Communication component 402 is arranged to bi-directionally communicatewith database 106 via a communication channel 126 and is arranged tobi-directionally communicate with communication component 112 via acommunication channel 128. Communication component 402 is additionallyarranged to directionally communicate with image data receivingcomponent 404 via a communication channel 412, to communicate withtraining data receiving component 406 via a communication channel 414and to communicate with segment data receiving component 408 via acommunication channel 416, to communicate with feature data receivingcomponent 410 via a communication channel 418. Communication component402 may be any device or system that is able to access data withindatabase 106 directly via communication channel 126 or indirectly, viacommunication channel 128, communication component 112, communicationchannel 138, network 104 and communication channel 140. Image datareceiving component 404, training data receiving component 406, segmentdata receiving component 408 and feature data receiving component 410may each be any device or system that is able to receive data fromcommunication component 402 and to output the received data.

Image data component 402 is additionally arranged to communicate withSIG component 114 and with GLC matrix component 116 via communicationchannel 130. Training data component 406 is additionally arranged tocommunicate with classification component 118 via communication channel132. Segment data receiving component 408 is additionally arranged tocommunicate with zonal statistics component 120 via communicationchannel 134. Feature data receiving component 410 is additionallyarranged to communicate with FIG component 122 via communication channel136. Communication channels 412, 414, 416 and 418 may be any known wiredor wireless communication channel.

Returning to FIG. 1, at this point accessing component 110 has receivedthe image data. An example of such image data will now be described withreference to FIG. 5.

FIG. 5 illustrates a satellite image 500 of a plot of land.

As shown in the figure, satellite image 500 includes: grassy areas, asample of which is labeled as grassy area 502; a plurality of trees, asample of which is labeled as trees 504; and a plurality of man-madesurfaces. The man-made surfaces include: a plurality of buildings, asample of which is indicated as building 506; a plurality of pavedsurfaces, a sample of which is indicated as road 508; and a plurality ofpools, a sample of which is indicated as pool 510.

As for a broad view of method 200, system 100 will be able to determinethe surface covering of land within satellite image 500, to determinedistinct segments of the area of land within satellite image 500, todetermine features within the area of land within satellite image 500and to provide an index of the features per segment of land, or asegment feature index, of the area of land within satellite image 500.The segment feature index may include: a primary feature index thatrelates to raw tallies of features from the feature data per landsegment, which will illustrate measured features per land segment; asecondary feature index that relates to predetermined Booleanrelationships of features from the feature data per land segment, whichwill illustrate predetermined associations of measured features per landsegment; and a tertiary feature index that relates to predeterminedlikelihoods of Boolean relationships of features from the feature dataper land segment, which will infer associations of measured features perland segment.

After the image data is received (S204), a surface index is generated(S206). For example, as shown in FIG. 1, accessing component 110provides the received image data to SIG component 114 via communicationchannel 130. As shown in FIG. 1 accessing component 110 retrieves imagedata from database 106. As shown in FIG. 3, database 106 provides theimage data from image data database 302. As shown in FIG. 4,communication component 402 receives the image data from image datadatabase 302 and provides the image data to image data receivingcomponent 404 via communication channel 414. Returning to FIG. 1, imagedata receiving component 404 (of accessing component 110) then providesthe image data to SIG component 114 via communication channel 130.

In an example embodiment, SIG component 114 generates surface index forthe image data by any known manner. A non-limiting example of a surfaceindex includes any known vegetation index. SIG component 114 thenprovides the surface index to classification component 118 viacommunication channel 142. For purposes of discussion herein, let thesurface index be a vegetation index.

Returning to FIG. 2, after the surface index is generated (S206),classification results are generated (S208). For example, as shown inFIG. 1, accessing component 110 provides the received image dataadditionally to classification component 118 via communication channel132. Further, SIG component 114 provides the surface index toclassification component 118 via communication line 142. With the imagedata from accessing component 110 and with the surface index from SIGcomponent, classification component 118 classifies each pixel of data asone of many predetermined classes.

For example, returning to FIG. 5, a pixel within image 500 at thelocation of trees 504 will have colors (frequencies) and intensitiesindicative of trees. As such, classification component will useinformation from the surface index in addition to the image data forthat pixel to classify the pixel as a tree. Similarly, a pixel withinimage 500 at the location of road 508 will have colors (frequencies) andintensities indicative of a road. As such, classification component willuse information from the surface index in addition to the image data forthat pixel to classify the pixel as a road. This classificationcontinues for each pixel within image 500.

Returning to FIG. 2, after the classification results are generated(S208), training data is received (S210). For example, as shown in FIG.1 accessing component 110 retrieves training data from database 106. Asshown in FIG. 3, database 106 provides the training data from trainingdata database 304. As shown in FIG. 4, communication component 402receives the training data from training data database 304 and providesthe training data to training data receiving component 406 viacommunication channel 414. Returning to FIG. 1, training data receivingcomponent 406 (of accessing component 110) then provides the trainingdata to classification component 118 via communication channel 132.

It should be noted that in the example discussed above, generating theclassification results (S208) is prior to receiving training data(S210). However, in some embodiments, generating the classificationresults (S208) may occur after receiving training data (S210). Further,in some embodiments, generating the classification results (S208) mayoccur concurrently with receiving training data (S210).

Returning to FIG. 2, after the training data is received (S210), a finalclassification is generated (S212). For example, in one embodiment,every pixel within the entire image 500 of FIG. 5 will have beenclassified by any known classification system or method. In anotherexample embodiment, groups of similar pixels are classified using GLCmatrix component 116. GLC matrix generation component 116 generates aGLC matrix and provides this matrix to classification component 118 viacommunication channel 144.

In one embodiment, classification component 118 classifies the pixelsusing any known classification system or method, non-limiting examplesof which include a CART classification, a Naïve Bayes classification, arandom forests classification, a GMO Max Entropy classification, an MCPclassification, a Pegasos classification, an IKPamir classification, avoting SVM classification, a margin SVM classification and a Winnowclassification.

CART (for Classification and Regression Trees) classification uses adecision tree as a predictive model which maps observations about anitem to conclusions about the item's target value.

Naïve Bayes classification may be any device or system that is able touse a simple probabilistic classifier based on applying Bayes' theoremwith strong (naive) independence assumptions between the features. NaiveBayes classification combines a Bayes classification model with adecision rule. Other example embodiments may use a Fast Naïve Bayesclassification, which works on binary or integer weighted features.

Random forests classification may be any device or system that is ableto employ an ensemble learning method for classification, regression andother tasks, and operates by constructing a multitude of decision treesat training time and outputting the class that is the mode of theclasses (classification) or mean prediction (regression) of theindividual trees.

GMO Max Entropy classification may be any device or system that is ableto use a multinomial logistic regression classification method thatgeneralizes logistic regression to multiclass problems, i.e. with morethan two possible discrete outcomes. In other words, GMO Max Entropyclassification uses a model that predicts the probabilities of thedifferent possible outcomes of a categorically distributed dependentvariable, given a set of independent variables (which may bereal-valued, binary-valued, categorical-valued, etc.).

MCP (for Multi Class Perceptron) classification may be any device orsystem that is a type of linear classifier and as such makes itspredictions based on a linear predictor function combining a set ofweights with the feature vector. MCP classification is used forsupervised classification.

Pegasos (for Primal Estimated sub-GrAdient SOlver for SVM)classification may be any device or system that is able to employ simpleand effective iterative algorithm for solving the optimization problemcast by Support Vector Machines (SVM). The method alternates betweenstochastic gradient descent steps and projection steps. The method wascreated by Shalev-Shwartz, Singer, and Srebro.

IKPamir (for Intersection Kernel Support Vector Machines) classificationmay be any device or system that is able to employ a non-linear SVMclassifier and uses histogram intersection kernels.

Voting SVM classification may be any device or system that is able toemploy, for the one-versus-one approach, classification by a max-winsvoting strategy. Specifically, every classifier assigns the instance toone of the two classes, then the vote for the assigned class isincreased by one vote, and finally the class with the most votesdetermines the instance classification.

Margin SVM classification may be any device or system that is able toconstruct a hyperplane or set of hyperplanes in a high- orinfinite-dimensional space, which can be used for classification,regression, or other tasks. Intuitively, a good separation is achievedby the hyperplane that has the largest distance to the nearest trainingdata point of any class (so-called functional margin), since in generalthe larger the margin the lower the generalization error of theclassifier. Margin SVM classification employs a linear SVM model.

Winnow classification may be any device or system that is able to use analgorithm similar to the perceptron algorithm. However, MCPclassification uses an additive weight-update scheme, whereas Winnowclassification uses a multiplicative scheme that allows it to performmuch better when many dimensions are irrelevant (hence its name).

In another embodiment, a classification component classifies the pixelsusing a voting system that takes into account a plurality of knownclassification system or method. This embodiment will be furtherdescribed with reference to FIG. 6.

FIG. 6 illustrates another example system 600 for managinggeodemographic data in accordance with aspects of the present invention.

As shown in the figure, system 600 includes many components of system100 of FIG. 1 discussed above. However, system 600 additionally includesa voting component 606. Further, classification component 118 of system100 is replaced with a classification component 604 in system 600.

In this example, database 106, controlling component 108, accessingcomponent 110, communication component 112, SIG component 114, GLCmatrix generation component 116, classification component 604, votingcomponent 606, zonal statistics component 120, FIG component 122 andcatalog component 124 are illustrated as individual devices. However, insome embodiments, at least two of database 106, controlling component108, accessing component 110, communication component 112, SIG component114, GLC matrix generation component 116, classification component 604,voting component 606, zonal statistics component 120, FIG component 122and catalog component 124 may be combined as a unitary device. Further,in some embodiments, at least one of database 106, controlling component108, accessing component 110, communication component 112, SIG component114, GLC matrix generation component 116, classification component 604,voting component 606, zonal statistics component 120, FIG component 122and catalog component 124 may be implemented as a computer havingtangible computer-readable media for carrying or havingcomputer-executable instructions or data structures stored thereon.

Voting component 606 is arranged to communicate with classificationcomponent 604 via a communication channel 608 and to communicate withzonal statistics component 120 via a communication channel 610. Votingcomponent 606 may be any device or system that is able to generate afinal surface cover classification based a majority vote of the surfacecover classifications generated by classification component 604.

Communication channel 608 and communication channel 610 may be any knownwired or wireless communication channel.

System 600 generally functions in a manner similar to system 100 of FIG.1, with the exception of how the final surface cover classification isdetermined. In system 600, the pixels are classified based on a votingsystem of classification component 604 and voting component 606. Thiswill be further described with reference to FIG. 7.

FIG. 7 illustrates an example of classification component 604 of FIG. 6,in accordance with aspects of the present invention.

As shown in FIG. 7, classification component 604 includes a plurality ofclassifying components 702 and voting component 606.

Classification component 604 includes a CART classifying component 708,a Naïve Bayes classifying component 710, a random forests classifyingcomponent 712, a GMO Max Entropy classifying component 714, an MCPclassifying component 716, a Pegasos classifying component 718, anIKPamir classifying component 720, a voting SVM classifying component722, a margin SVM classifying component 724 and a Winnow classifyingcomponent 726. It should be noted, that any number of classifyingcomponents may be used in accordance with aspects of the presentinvention, wherein those listed in classification component 604 aremerely non-limiting examples used for purposes of discussion.

In this example, CART classifying component 708, Naïve Bayes classifyingcomponent 710, random forests classifying component 712, GMO Max Entropyclassifying component 714, MCP classifying component 716, Pegasosclassifying component 718, IKPamir classifying component 720, voting SVMclassifying component 722, margin SVM classifying component 724 andWinnow classifying component 726 are illustrated as individual devices.However, in some embodiments, at least two of CART classifying component708, Naïve Bayes classifying component 710, random forests classifyingcomponent 712, GMO Max Entropy classifying component 714, MCPclassifying component 716, Pegasos classifying component 718, IKPamirclassifying component 720, voting SVM classifying component 722, marginSVM classifying component 724 and Winnow classifying component 726 maybe combined as a unitary device. Further, in some embodiments, at leastone of CART classifying component 708, Naïve Bayes classifying component710, random forests classifying component 712, GMO Max Entropyclassifying component 714, MCP classifying component 716, Pegasosclassifying component 718, IKPamir classifying component 720, voting SVMclassifying component 722, margin SVM classifying component 724 andWinnow classifying component 726 may be implemented as a computer havingtangible computer-readable media for carrying or havingcomputer-executable instructions or data structures stored thereon.

In the example embodiment of FIG. 7, classification component 604includes 10 distinct classifying components. It should be noted that anynumber of distinct classifying components equal to or greater than threemay be used. The reason that at least three classifying components areused is that the final classification per pixel is based on a majorityvote of at least some of the classifying components.

For example, for purposes of discussion, consider classificationcomponent 604 of FIG. 7. Further, returning to FIG. 5, let a pixelwithin image 500 at the location of trees 504, be classified by each ofCART classifying component 708, Naïve Bayes classifying component 710,random forests classifying component 712, GMO Max Entropy classifyingcomponent 714, MCP classifying component 716, Pegasos classifyingcomponent 718, IKPamir classifying component 720, voting SVM classifyingcomponent 722, margin SVM classifying component 724 and Winnowclassifying component 726. Further, as discussed above, in accordancewith aspects of the present invention, each classification is performedwith additional reference to the group results generated by GLC matrixcomponent 116 to further reduce the likelihood of an erroneousclassification.

Each classifying method may have specific strengths and weaknesses,wherein some instances of classification are more reliable than others.In this example, for purposes of discussion, presume that CARTclassifying component 708, Naïve Bayes classifying component 710, randomforests classifying component 712, GMO Max Entropy classifying component714 and MCP classifying component 716 correctly classify the pixelwithin image 500 at the location of trees 504 as corresponding to atree. Further, presume that Pegasos classifying component 718, IKPamirclassifying component 720, and voting SVM classifying component 722incorrectly classify the pixel within image 500 at the location of trees504 as corresponding to artificial turf. Finally, presume that marginSVM classifying component 724 and Winnow classifying component 726incorrectly classify the pixel within image 500 at the location of trees504 as corresponding to a road.

In this example, clearly there is not 100% agreement between all theclassifying components. However, a majority vote of the classificationswill increase likelihood of a correct classification.

As shown in FIG. 7, the classifying components provide their respectiveclassifications to voting component 606 via communication channel 608.In some embodiments, the distinct classifications are provided to votingcomponent 606 in a serial manner. In some embodiments, the distinctclassifications are provided to voting component 606 in parallel.

Voting component 606 tallies the classifications for the pixels andgenerates a final classification for the pixels based on a majority voteof the individual classifications. Using the example discussed above, 5classifying components classify the pixels within image 500 at thelocation of trees 504 as corresponding to a tree, 3 classifyingcomponents classify the pixel within image 500 at the location of trees504 as corresponding to artificial turf and 2 classifying componentsclassify the pixel within image 500 at the location of trees 504 ascorresponding to a road. In this example, the 5 classifying componentsthat classified the pixels within image 500 at the location of trees 504as corresponding to a tree are a majority as compared to the 3classifying components that classified the pixels within image 500 atthe location of trees 504 as corresponding to artificial turf and ascompared to the 2 classifying components that classified the pixelswithin image 500 at the location of trees 504 as corresponding to aroad. Therefore, voting component 606 would generate the finalclassification of the pixels within image 500 at the location of trees504 as corresponding to a tree.

In some embodiments, voting component 606 considers the classificationsfrom all classifying components within classification component 604. Inother embodiments, voting component 606 may consider the classificationsfrom less than all classifying components within classificationcomponent 604, so long as the number of classifications is equal to orgreater than three. In this manner, voting component 606 will avoid thesituation where two classifying components each provide differentclassifications for the same image pixels, so there cannot be amajority.

FIG. 8 illustrates a classified image 800 of the plot of land withinsatellite image 500 of FIG. 5.

As shown in FIG. 8, classified image 800 includes an area 802, an area804, an area 806, an area 808 and an area 810. Area 802 corresponds tograss 502 of satellite image 500 of FIG. 5. Area 804 corresponds totrees 504 of satellite image 500 of FIG. 5. Area 806 corresponds tobuilding 506 of satellite image 500 of FIG. 5. Area 808 corresponds toroad 508 of satellite image 500 of FIG. 5. Area 810 corresponds toman-made pool 510 of satellite image 500 of FIG. 5.

Returning to FIG. 2, after the final classification is generated (S212),segment data is received (S214). For example, as shown in FIG. 1,accessing component 110 provides the segment data to zonal statisticscomponent 120 via communication channel 134. For example, as shown inFIG. 1 accessing component 110 retrieves segment data from database 106.As shown in FIG. 3, database 106 provides the segment data from segmentdata database 306. As shown in FIG. 4, communication component 402receives the segment data from segment data database 306 and providesthe segment data to segment data receiving component 408 viacommunication channel 416. Returning to FIG. 1, segment data receivingcomponent 408 (of accessing component 110) then provides the segmentdata to zonal statistics component 120 via communication channel 134.

At this point, the boundaries of surface are known by way of the segmentdata. These boundaries may include country boundaries, state boundaries,county boundaries, city/town boundaries and boundaries of individuallyowned parcels of land. These boundaries may be provided by governmententities and/or private entities. Zonal statistics component 120 may usethe boundaries as identified in the segment data to establish thesurface cover per segment of land.

Returning to FIG. 2, after the segment data is received (S214) and thesurface cover has been classified per segment of land, the surface coverby segment is generated (S216). For example, as shown in FIG. 1

Zonal statistics component 120 then generates the surface coverclassification per segment of land. For example, if the image data wereto include the image of an entire state, zonal statistics component 120may be able to generate the surface cover classification per county, pertown, or even per parcel of land by organizing the surface coverclassification per county, per town, etc. More particularly, in someembodiments polygons may be drawn around each land cover type. The endresult is a vector layer of land cover polygons that are then used tocalculate area. Zonal statistics is not often used, but is used in moregeneral remote sensing applications. The biggest difference is thatzonal statistics are derived directly from the imagery. On the otherhand, surface cover calculation using vector layers has an intermediarystep of transforming the image into a vector layer for each surfacecover type, and then the area for each vector layer is calculated withinthe parcel.

Returning to FIG. 2, after the surface cover by parcel is generated(S216), the feature data is received (S218). For example, as shown inFIG. 1, accessing component 110 provides the feature data to FIGcomponent 122 via communication channel 136. For example, as shown inFIG. 1 accessing component 110 retrieves feature data from database 106.As shown in FIG. 3, database 106 provides the feature data from featuredata database 308. As shown in FIG. 4, communication component 402receives the feature data from feature data database 308 and providesthe feature data to feature data receiving component 410 viacommunication channel 418. Returning to FIG. 1, feature data receivingcomponent 410 (of accessing component 110) then provides the featuredata to FIG component 122 via communication channel 136.

Feature data may be any data associated with a geographic region,non-limiting examples of which include demographics, climate, weather,marketing, land cover attributes and correlations between them. In onenon-limiting example, feature data may include population data withinthe geographic region broken down into age categories. In anothernon-limiting example, feature data may include population data withinthe geographic region broken down into income categories. In anothernon-limiting example, feature data may include a number of in-groundpools within the geographic region. In another non-limiting example,feature data may include the number of houses within the geographicregion that have a paved driveway. In another non-limiting example,feature data may include the number of houses within the geographicregion that have an in-ground pool and a paved driveway.

Returning to FIG. 2, after the feature data is received (S218), thefeature index is generated (S220). For example, as shown in FIG. 1accessing component 110 retrieves training data from database 106. Asshown in FIG. 3, database 106 provides the feature data from featuredata database 308. As shown in FIG. 4, communication component 402receives the feature data from feature data database 308 and providesthe feature data to feature data receiving component 410 viacommunication channel 418. Returning to FIG. 1, feature data receivingcomponent 410 (of accessing component 110) then provides the featuredata to FIG component 122 via communication channel 136. FIG component122 determines a feature index based on the feature data.

FIG. 9 illustrates an example FIG component 122 in accordance withaspects of the present invention.

As shown in the figure, FIG component 122 includes a clusteringcomponent 902, a rule component 904, a primary index generator 906, asecondary index generator 908, a tertiary index generator 910 and anoutput component 912.

In this example, rule component 904, primary index generator 906,secondary index generator 908, tertiary index generator 910 and outputcomponent 912 are illustrated as individual devices. However, in someembodiments, at least two of rule component 904, primary index generator906, secondary index generator 908, tertiary index generator 910 andoutput component 912 may be combined as a unitary device. Further, insome embodiments, at least one of rule component 904, primary indexgenerator 906, secondary index generator 908, tertiary index generator910 and output component 912 may be implemented as a computer havingtangible computer-readable media for carrying or havingcomputer-executable instructions or data structures stored thereon.

Clustering component 902 is arranged to communicate with accessingcomponent 110 via communication channel 136 and is arranged tocommunicate with primary index generator 906 via a communication channel914. Clustering component 902 may be any device or system that is ableto tally feature data within the geographical region.

Primary index generator 906 is additionally arranged to communicate withoutput component 912 via a communication channel 916, to communicatewith secondary index generator 908 via a communication channel 918 andto communicate with zonal statistics component 120 (not shown) viacommunication channel 148. Primary index generator 906 may be any deviceor system that is able to generate a primary index based on the talliedfeature data and the zonal statistics data.

Rule component 904 is arranged to communicate with accessing component110 via communication channel 136, is arranged to communicate withsecondary index generator 908 via a communication channel 920 and isarranged to communicate with tertiary index generator 910 via acommunication channel 926. Rule component 904 may be any device orsystem that is able to create, or have stored therein, rules forassociating the feature data.

Secondary index generator 908 is additionally arranged to communicatewith output component 912 via a communication channel 924, tocommunicate with tertiary index generator 910 via a communicationchannel 926 and to communicate with zonal statistics component 120 (notshown) via communication channel 148. Secondary index generator 908 maybe any device or system that is able to generate a secondary index basedon predetermined Boolean associations of the primary index.

Tertiary index generator 910 is additionally arranged to communicatewith output component 912 via a communication channel 928 and tocommunicate with zonal statistics component 120 (not shown) viacommunication channel 148. Tertiary index generator 910 may be anydevice or system that is able to generate a tertiary index based on apredetermined threshold of a second set of Boolean associations of thefeature index.

Output component is additionally arranged to communicate with zonalstatistics component 120 (not shown) via communication channel 148 andto communicate with catalog component 124 via communication channel 150.

Communication channels 914, 916, 918, 920, 922, 924, 926 and 928 may beany known wired or wireless communication channel.

In operation, clustering component 902 and rule component 904 receivefeature data from accessing component 110 via communication channel 136.

Clustering component 906 clusters portions of the feature data based onpredetermined features. In example embodiment, clustering component 906tallies discrete data entries within the geographical area of the imagedata. Such tallies may be associated with any component within thefeature data. For example, data supplied by a census database mayprovide tallies of population by age, race, income, etc., whereas datasupplied by a local zoning board may provide tallies of parcels of landclassified as residential, commercial or industrial and whereas weatherdata supplied by weather station my provide tallies on the number ofdays of rain, snow or sunshine. Clustering component 906 provides theraw tallies to primary index generator 906 via communication channel914.

Primary index generator 906 generates a primary feature index based onthe raw tallies of the feature data from clustering component 902 andthe segment land cover classification from zonal statistics component120.

In particular, zonal statistics component 120 provides zonal statisticswithin a segment of land cover. For example, zonal statistics component120 provides the segment land cover classification so as to indicatee.g., number of houses per parcel within the geographic area, the areaof paved streets within the geographic area and number of paveddriveways per parcel within the geographic area, etc. As discussedabove, this segment land cover classification is generated from suppliedzonal statistics data that is fused with surface area classificationdata that is derived from image data of the geographic area, as shownfor example with reference to FIG. 8.

Primary index generator 906 then fuses the segment land coverclassification with the raw tallies of the feature data. For example,for purpose of discussion, let examples of raw tallies of the featuredata include a total number of households within a geographic area, atotal population within a geographic area, and the average dailyrainfall within the geographic area. In such an example, primary indexgenerator 906 may generates a primary feature index so as to indicatee.g., the average number of persons (based on the given number ofhouses) per parcel within the geographic area, the average amount ofevaporated water (based on the area of paved streets and the number ofpaved driveways) within the geographic area, etc. In this manner, thegenerated primary index is strictly based on the raw tallies of thefeature data per segment of land within the geographic area.

Primary index generator 906 outputs the generated primary feature indexto output component 912 via communication channel 916 and to secondaryindex generator 908 via communication channel 918.

Secondary index generator 908 generates a secondary feature index basedon the primary feature index from primary index generator 906, theprimary rules provided by rule component 904.

In particular, rule component 904 has primary rules for associating thefeature data as provided by accessing component 110. Non-limitingexamples of such primary rules include Boolean operations of types offeature data. For example, suppose data supplied by a census databasemay provide tallies of population by age, race, income, etc., whereasdata supplied by a local zoning board may provide tallies of parcels ofland classified as residential, commercial or industrial and whereasweather data supplied by weather station my provide tallies on thenumber of days of rain, snow or sunshine. Non-limiting example primaryrules may include; houses AND population density greater than apredetermined population density threshold; houses NOT with an averageincome lower than $50,000 per year, etc.

As discussed above zonal statistics component 120 has provided zonalstatistics within a segment of land cover by way of the primary indexfrom primary index generator 906. Again, suppose for example that zonalstatistics component 120 provides the segment land cover classificationso as to indicate e.g., number of houses per parcel within thegeographic area, the area of paved streets within the geographic areaand number of paved driveways per parcel within the geographic area,etc.

Secondary index generator 908 then tallies predetermined Booleanoperations of the primary index. In other words, secondary indexgenerator 908 ultimately fuses the segment land cover classificationwith the feature data that has been associated by the primary rules ofrule component 904. For example, for purpose of discussion, let examplesassociated feature data as associated by the primary rules of rulecomponent 904 include a total number of households within a geographicarea that have an average income greater than $200,000 per year. In suchan example, secondary index generator 908 may generates a secondaryfeature index so as to indicate e.g., the average number of households(based on the given number of persons) per parcel within the geographicarea, etc. In this manner, the generated secondary index is based onpredetermined Boolean associations the feature data per segment of landwithin the geographic area.

Secondary index generator 908 outputs the generated secondary featureindex to output component 912 via communication channel 924 and totertiary index generator 910 via communication channel 926.

Tertiary index generator 910 generates a tertiary feature index based onthe secondary feature index from secondary index generator 908 and thesecondary rules provided by rule component 904. In short, tertiary indexgenerator 910 may be considered an inference engine, in that inferencesare determined based on predetermined thresholds of calculatedlikelihoods of the predetermined Boolean operations.

In particular, rule component 904 has secondary rules for associatingthe feature data as provided by accessing component 110. Non-limitingexamples of such secondary rules include predetermined statisticalthresholds of Boolean operations of types of feature data. For example,suppose data supplied by a census database may provide tallies ofpopulation by age, race, income, etc., whereas data supplied by a localzoning board may provide tallies of parcels of land classified asresidential, commercial or industrial and whereas weather data suppliedby weather station my provide tallies on the number of days of rain,snow or sunshine. Non-limiting example secondary rules may include; anBoolean association of types of feature data that is greater than 60%.After all predetermined possible Boolean relationships are determined,those that have an association that is greater than 60% will beincluded. Such an example relationship may include population density ofgreater than 8 per acre AND houses with an average income lower than$50,000 per year AND annual death rates of 15% within the geographicarea, etc.

As discussed above zonal statistics component 120 provides zonalstatistics within a segment of land cover. Again, suppose for examplethat zonal statistics component 120 provides the segment land coverclassification so as to indicate e.g., number of houses per parcelwithin the geographic area, the area of paved streets within thegeographic area and number of paved driveways per parcel within thegeographic area, etc.

Tertiary index generator 910 then tallies Boolean operations of theprimary index that provide a likelihood greater than a predeterminedthreshold. In other words, tertiary index generator 910 ultimately thesegment land cover classification with the feature data that has beenassociated by the secondary rules of rule component 904. For example,for purpose of discussion, let examples associated feature data asassociated by the secondary rules of rule component 904 includepopulation density of greater than 8 per acre AND houses with an averageincome lower than $50,000 per year AND annual death rates of 15% withinthe geographic area. In such an example, tertiary index generator 910may generate a tertiary feature index so as to indicate e.g., theaverage number of high population and low-income households that liverelatively long lifespans per parcel within the geographic area, etc. Inthis manner, the generated tertiary index is based on a predeterminedlikelihood of Boolean associations the feature data per segment of landwithin the geographic area.

Tertiary index generator 910 outputs the generated tertiary featureindex to output component 912 via communication channel 928.

At this point, output component has received the primary feature indexfrom primary index generator 906, the secondary feature index fromsecondary index generator 908 and the tertiary feature index fromtertiary index generator 910. The primary feature index relates to theraw tallies of features from the feature data per land segment, whichwill illustrate measured features per land segment. The secondaryfeature index relates to predetermined Boolean relationships of featuresfrom the feature data per land segment, which will illustratepredetermined associations of measured features per land segment. Thetertiary feature index relates to predetermined likelihoods of Booleanrelationships of features from the feature data per land segment, whichwill infer associations of measured features per land segment.

For example, for purposes of discussion, let the plot of land withinimage 500 of FIG. 5 be a delineated parcel of land.

At this point of method 200, surface cover of the parcel of land withinimage 500 has been determined. As shown in FIG. 1, zonal statisticscomponent 120 provides the surface cover of the parcel of land to FIGcomponent 122 via communication channel 148. Further, the feature datais known from feature data database 308. As such, the features of theplot of land within image 500 of FIG. 5 have determined.

Features per segment of land may then be determined.

Returning to FIG. 2, after the feature index is generated (S220), acatalog is generated (S222). For example, as shown to FIG. 1, FIGcomponent 122 outputs the feature index to catalog component 124 viacommunication channel 150. In some embodiments, catalog component 124may output the primary feature index as a catalog. In some embodiments,catalog component 124 may output the secondary feature index as acatalog. In some embodiments, catalog component 124 may output thetertiary feature index as a catalog. In some embodiments, catalogcomponent 124 may output a combination of at least two of the primaryfeature index, the secondary feature index and the tertiary featureindex as a catalog.

Catalog component 124 generates a catalog based on outputs the segmentfeature index. The catalog is a taxonomy of the data provided by FIGcomponent 122. For example, from the primary feature index, the catalogmay include strict tallies of classifications of types of data per landsegment, e.g., number of homes, number of pools, square feet of pavedroads, square feet of grass, square feet of tree cover, etc. Further,for example, from the secondary feature index, the catalog may includetallies of predetermined associations of data per land segment, e.g.,number of homes AND pools, lots greater than 1 acre AND an annual incomegreater than $200,000, etc. Still further, for example, from thetertiary feature index, the catalog may include tallies of associationsof data per land segment that have a statistical likelihood of greaterthan a predetermined threshold, e.g., lots greater than 1 acre AND an ahouse having a roof area greater than 1200 square feet and annual incomegreater than $150,000 are 75% likely to purchase solar panels, etc.

Catalog component 124 outputs the catalog to communication component112. Communication component 112 may then provide the catalog to network104. From network 104, the catalog associated with a geographic area maybe accessible.

Returning to FIG. 2, after catalog is generated (S222), method 200 stops(S224).

It should be noted that in the non-limiting example embodimentsdiscussed above with reference to FIGS. 1 and 6, surface coverclassifications are generated and provided to zonal statistics component120. However, in other embodiments, such surface cover classificationsmay be provided by any known manner, including a direct import from adatabase. The novel features of the invention, including determiningsuch surface cover classifications per land segment and fusing suchsurface cover classifications per land segment with feature data togenerate a catalog, may then be used in conjunction with the providedsurface cover classifications.

In accordance with aspects of the present invention, a system and methodfor managing geodemographic data uses a plurality of classifyingcomponents to classify the image data. A majority voting mechanismincreases the likelihood for accuracy of classification of the imagedata. Further, in accordance with aspects of the present invention, azonal statistics component provides classification of the image data persegment of land within the image data. Still further, feature data isused to generate a feature index. This feature index is then used withthe classification of the image data per segment of land to determinespecific features within per segment of land.

In the drawings and specification, there have been disclosed embodimentsof the invention and, although specific terms are employed, they areused in a generic and descriptive sense only and not for purposes oflimitation, the scope of the invention being set forth in the followingclaims.

1. A computer-implemented method for image analysis by one or morecomputer executing executable instructions stored in one or morenon-transitory, tangible, computer readable media, the methodcomprising: receiving one or more multiband image of a geographicregion, the one or more multiband image having pixels; generating asurface index for the one or more multiband image containing informationindicative of a surface type represented by one or more of the pixels inthe one or more multiband image; classifying the pixels of the one ormore multiband image into one of a group of predefined land coverclasses, based on the surface index; receiving information indicative offeatures within a first multiband image of the one or more multibandimage; dividing the first multiband image into segments; and creating asegment feature index of the features for each segment, wherein thesegment feature index comprises a primary feature index, a secondaryfeature index, and a tertiary feature index.
 2. The computer-implementedmethod of claim 1, wherein generating the surface index includesgenerating a vegetation index for the multiband image containinginformation indicative of whether one or more of the pixels in the oneor more multiband image represents vegetation.
 3. Thecomputer-implemented method of claim 1, wherein classifying pixels inthe one or more multiband image into one of a group of predefined landcover classes, based on the surface index, further comprises classifyingpixels in the one or more multiband image into one of the group ofpredefined land cover classes, based on the surface index and based on amajority vote of results of a plurality of classification algorithmsdetermining a plurality of surface cover classifications of the pixels.4. The computer-implemented method of claim 3, wherein the plurality ofclassification algorithms include three or more of a CARTclassification, a Naïve Bayes classification, a random forestsclassification, a GMO Max Entropy classification, an MCP classification,a Pegasos classification, an IKPamir classification, a voting SVMclassification, a margin SVM classification, and a Winnowclassification.
 5. The computer-implemented method of claim 1, furthercomprising: dividing the pixels of a first multiband image of the one ormore multiband image into two or more segments of the first multibandimage; and generating a land cover classification for each of the two ormore segments of the first multiband image based on the classificationof the pixels.
 6. The computer-implemented method of claim 1, furthercomprising: dividing the pixels of a first multiband image of the one ormore multiband image into a first class image and a second class image,wherein classifying the pixels of the first multiband image comprisesclassifying pixels of the first class image and classifying pixels ofthe second class image; and reassembling the first multiband image fromthe first class image and the second class image.
 7. Thecomputer-implemented method of claim 1, wherein the primary featureindex includes a number of measured features per segment; wherein thesecondary feature index includes predetermined Boolean relationships offeatures of each segment; and wherein the tertiary feature indexincludes predetermined likelihoods of Boolean relationships of featuresof each segment and a predetermined threshold of the predeterminedlikelihoods.
 8. The computer-implemented method of claim 7, wherein theinformation indicative of features includes one or more of land coverattributes information, demographic information, man-made structureinformation, and weather information.
 9. The computer-implemented methodof claim 1, further comprising: generating land cover classificationsfor one or more geographic area based on the classification of thepixels in a first multiband image and a second multiband image of theone or more multiband image.
 10. A non-transitory, tangible,computer-readable media having computer-readable instructions storedthereon, for use with a computer and being capable of instructing thecomputer to perform a method comprising: receiving one or more multibandimage of a geographic region, the one or more multiband image havingpixels; generating a surface index for the one or more multiband imagecontaining information indicative of a surface type represented by oneor more of the pixels in the one or more multiband image; classifyingthe pixels of the one or more multiband image into one of a group ofpredefined land cover classes, based on the surface index; receivinginformation indicative of features within a first multiband image of theone or more multiband image; dividing the first multiband image intosegments; and creating a segment feature index of the features for eachsegment, wherein the segment feature index comprises a primary featureindex, a secondary feature index, and a tertiary feature index.
 11. Thenon-transitory, tangible, computer-readable media of claim 10, whereingenerating the surface index includes generating a vegetation index forthe multiband image containing information indicative of whether one ormore of the pixels in the one or more multiband image representsvegetation.
 12. The non-transitory, tangible, computer-readable media ofclaim 10, wherein classifying pixels in the one or more multiband imageinto one of a group of predefined land cover classes, based on thesurface index, further comprises classifying pixels in the one or moremultiband image into one of the group of predefined land cover classes,based on the surface index and based on a majority vote of results of aplurality of classification algorithms determining a plurality ofsurface cover classifications of the pixels.
 13. The non-transitory,tangible, computer-readable media of claim 12, wherein the plurality ofclassification algorithms include three or more of a CARTclassification, a Naïve Bayes classification, a random forestsclassification, a GMO Max Entropy classification, an MCP classification,a Pegasos classification, an IKPamir classification, a voting SVMclassification, a margin SVM classification, and a Winnowclassification.
 14. The non-transitory, tangible, computer-readablemedia of claim 10, further comprising: dividing the pixels of a firstmultiband image of the one or more multiband image into two or moresegments of the first multiband image; and generating a land coverclassification for each of the two or more segments of the firstmultiband image based on the classification of the pixels.
 15. Thenon-transitory, tangible, computer-readable media of claim 10, furthercomprising: dividing the pixels of a first multiband image of the one ormore multiband image into a first class image and a second class image,wherein classifying the pixels of the first multiband image comprisesclassifying pixels of the first class image and classifying pixels ofthe second class image; and reassembling the first multiband image fromthe first class image and the second class image.
 16. Thenon-transitory, tangible, computer-readable media of claim 10: whereinthe primary feature index includes a number of measured features persegment; wherein the secondary feature index includes predeterminedBoolean relationships of features of each segment; and wherein thetertiary feature index includes predetermined likelihoods of Booleanrelationships of features of each segment and a predetermined thresholdof the predetermined likelihoods.
 17. The non-transitory, tangible,computer-readable media of claim 16, wherein the information indicativeof features includes one or more of land cover attributes information,demographic information, man-made structure information, and weatherinformation.
 18. The non-transitory, tangible, computer-readable mediaof claim 10, further comprising: generating land cover classificationsfor one or more geographic area based on the classification of thepixels in a first multiband image and a second multiband image of theone or more multiband image.